Follow our blog and get to know about the latest updates in the fields of technology like Python, Machine Learning, Data structures, Data Science, Digital Marketing etc.

How Machine Learning Works?


Computers can learn just like humans!

Machine learning is the branch of artificial intelligence (AI) which allows computers to function without being programmed to do so. It makes it easier to analyze and interpret massive amounts of data, which would otherwise take forever for humans to decode. 

In other words, machine learning is an effort to teach computers to think, learn, and act like humans. Thanks to increasing internet speeds, progress in storage technology, and expanding computational power, machine learning has exponentially advanced and become an essential part of almost every industry.

Machine learning (ML) is the method of using mathematical principles of data to assist a computer to learn without immediate instruction. Machine learning makes use of algorithms to recognize data patterns, and these patterns are then used to create a data model that is used to make forecasts. With increased data and knowledge, the results of machine learning are more reliable, much like how humans get better with more practice.

The adaptability of machine learning makes it a great choice in situations where the data is always changing, the nature of the requested task is always changing, or coding a solution would be completely impossible.

Wonder what the machine learning process looks like? Wonder no more, here is an overview of ML process:

Step 1: Gather and prepare the data

Once data sources are recognized, available data is compiled. This process depends on your project and data type. As you review your data, anomalies are identified, the structure is developed, and data integrity issues are resolved.

Step 2: Examine the model

Two groups: the training set and the test set are created from prepared data. The training set is a huge portion of your data that’s used to tune your machine learning models to the highest precision.

Step 3: Verify the model

When you’re ready to select your final data model, the test set is used to evaluate the performance and precision of the model using some performance metrics.

Step 4: Interpret the outcomes

Review the outcome to find insights, conclude, and predict outcomes.

Here’s an example...

Consider the following sequence.

5 - 25

6 - 36

7 - 49

So if you were given the number 8, which number would you pick so that the pair would match the above sequence?

If you decided that it’s 64, how did you do it?

You probably analyzed the past data (historical data) and "predicted" the number with the highest probability. A machine learning model is no different. It learns from experience and uses the collected information to make better forecasts.

In reality, machine learning is pure mathematics. Any and every machine learning algorithm is built nearby a mathematical function that can be modified. This also indicates that the learning method in machine learning is also based on mathematics.

Varieties of Machine Learning methods

Machine learning can be categorised as: supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised learning

Supervised learning is a machine learning strategy in which a data scientist acts like a tutor and trains the AI system by feeding basic rules and specified datasets. 

Supervised learning algorithms learn by example. Such examples are collectively referred to as training data. Once a machine learning model is prepared using the training dataset, it's given the test data to define the model's accuracy. Supervised learning can be further classified into two types: classification and regression.

Unsupervised learning

Unsupervised learning studies how systems can understand a function to describe a mysterious structure from unlabelled data. The system doesn’t figure out the right output, but it investigates the data and can draw results from datasets to describe hidden structures from unlabelled dataUnsupervised learning problems are generally grouped into clustering and association problems.

Semi-supervised learning

Semi-supervised learning is a merge of supervised and unsupervised learning. In this machine learning process, the data scientist trains the system just a little bit so that it gets a high-level overview. Also, a small percentage of the training data will be labeled, and the leftover will be unlabelled. Unlike supervised learning, this learning method requires the system to learn the rules and strategy by recognizing patterns in the dataset.

Semi-supervised learning is useful when you don't have enough labeled data, or the labeling process is expensive, but you want to create an actual machine learning model.

Reinforcement learning

Reinforcement learning (RL) is a learning method that combines with its environment by performing actions and discovers faults or rewards. Some of the most relevant features of reinforcement learning - Trial and error search and delayed reward. This method provides machine and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is most satisfying, this is known as the reinforcement signal.

Machine Learning – learning the human world

Machine Learning is a field that can guide us towards making effective choices in our daily activities. It is going to be very helpful in assisting individuals and companies to make smart decisions in the future. For example, classification can tell us whether or not to invest in a certain business and regression tells us how much we are likely to make if we invest in that business.

Along with recommending the products and services we will more likely to enjoy, it protects us from online fraudsters and keeps our email inbox clean of spam messages.

In short, it's a learning process that helps machines in knowing the human world around them.